EResNet-SVM: an overfitting-relieved deep learning model for recognition of plant diseases and pests.

Journal: Journal of the science of food and agriculture
PMID: 38483173

Abstract

BACKGROUND: The accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. As a result of the phenotype similarity of the hazarded plant after plant diseases and pests occur, as well as the interference of the external environment, traditional deep learning models often face the overfitting problem in phenotype recognition of plant diseases and pests, which leads to not only the slow convergence speed of the network, but also low recognition accuracy.

Authors

  • Haitao Xiong
    School of International Economics and Management, Beijing Technology and Business University, Beijing 100048, China.
  • Juan Li
    Department of Hygienic Inspection, School of Public Health, Jilin University 1163 Xinmin Street Changchun 130021 Jilin China songxiuling@jlu.edu.cn li_juan@jlu.edu.cn jinmh@jlu.edu.cn +86 43185619441.
  • Tiewei Wang
    College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao, China.
  • Fan Zhang
    Department of Anesthesiology, Bishan Hospital of Chongqing Medical University, Chongqing, China.
  • Ziyang Wang

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